Overview

Dataset statistics

Number of variables13
Number of observations23446
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory2.5 MiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric12

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
chlorophyll is highly skewed (γ1 = 51.65583126)Skewed
nitrate_mean has 767 (3.3%) zerosZeros
chlorophyll has 1529 (6.5%) zerosZeros

Reproduction

Analysis started2023-02-02 21:39:07.597779
Analysis finished2023-02-02 21:39:17.550002
Duration9.95 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

siteid
Categorical

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size366.3 KiB
ARIK
 
1096
LECO
 
1096
WALK
 
1096
SYCA
 
1096
PRIN
 
1096
Other values (19)
17966 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters93784
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARIK
2nd rowARIK
3rd rowARIK
4th rowARIK
5th rowARIK

Common Values

ValueCountFrequency (%)
ARIK 1096
 
4.7%
LECO 1096
 
4.7%
WALK 1096
 
4.7%
SYCA 1096
 
4.7%
PRIN 1096
 
4.7%
POSE 1096
 
4.7%
MCRA 1096
 
4.7%
MCDI 1096
 
4.7%
MAYF 1096
 
4.7%
LEWI 1096
 
4.7%
Other values (14) 12486
53.3%

Length

2023-02-02T16:39:17.680002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arik 1096
 
4.7%
mayf 1096
 
4.7%
como 1096
 
4.7%
leco 1096
 
4.7%
king 1096
 
4.7%
wlou 1096
 
4.7%
lewi 1096
 
4.7%
hopb 1096
 
4.7%
mcdi 1096
 
4.7%
mcra 1096
 
4.7%
Other values (14) 12486
53.3%

Most occurring characters

ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 93784
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 93784
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

nitrate_mean
Real number (ℝ)

Distinct11109
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.413306
Minimum0
Maximum851.7
Zeros767
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:17.732480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32083333
Q12.7708333
median6.1
Q321.471615
95-th percentile169.37135
Maximum851.7
Range851.7
Interquartile range (IQR)18.700781

Descriptive statistics

Standard deviation45.372328
Coefficient of variation (CV)2.0243478
Kurtosis14.711481
Mean22.413306
Median Absolute Deviation (MAD)4.7177083
Skewness3.4880563
Sum525502.38
Variance2058.6481
MonotonicityNot monotonic
2023-02-02T16:39:17.792379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 767
 
3.3%
3.3 33
 
0.1%
6.1 32
 
0.1%
3.55 32
 
0.1%
3.633333333 29
 
0.1%
3.65 29
 
0.1%
2.75 29
 
0.1%
3.14375 27
 
0.1%
4.55 26
 
0.1%
2.652083333 25
 
0.1%
Other values (11099) 22417
95.6%
ValueCountFrequency (%)
0 767
3.3%
0.001041666667 1
 
< 0.1%
0.002222222222 1
 
< 0.1%
0.003333333333 1
 
< 0.1%
0.007291666667 1
 
< 0.1%
0.008333333333 1
 
< 0.1%
0.00989010989 1
 
< 0.1%
0.01041666667 1
 
< 0.1%
0.01136363636 1
 
< 0.1%
0.01354166667 3
 
< 0.1%
ValueCountFrequency (%)
851.7 1
< 0.1%
493.6 1
< 0.1%
284.4541667 1
< 0.1%
264.3375 1
< 0.1%
261.76875 1
< 0.1%
261.3 1
< 0.1%
260.85625 1
< 0.1%
260.5569231 1
< 0.1%
259.7614583 1
< 0.1%
259.6916667 1
< 0.1%

specific_conductance
Real number (ℝ)

Distinct19471
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean583.87356
Minimum6.25 × 10-5
Maximum215585.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:17.855376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.25 × 10-5
5-th percentile13.48777
Q137.768763
median117.8826
Q3508.94584
95-th percentile685.11138
Maximum215585.2
Range215585.2
Interquartile range (IQR)471.17707

Descriptive statistics

Standard deviation6249.5379
Coefficient of variation (CV)10.703581
Kurtosis383.47817
Mean583.87356
Median Absolute Deviation (MAD)98.911444
Skewness19.25644
Sum13689500
Variance39056724
MonotonicityNot monotonic
2023-02-02T16:39:17.912917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
573.7750452 11
 
< 0.1%
34.77873521 11
 
< 0.1%
39.03122434 10
 
< 0.1%
37.93636806 10
 
< 0.1%
38.13544444 9
 
< 0.1%
466.7478697 9
 
< 0.1%
652.2436736 9
 
< 0.1%
38.02676389 9
 
< 0.1%
206.4707743 9
 
< 0.1%
111.3831285 9
 
< 0.1%
Other values (19461) 23350
99.6%
ValueCountFrequency (%)
6.25 × 10-51
< 0.1%
0.001541666667 1
< 0.1%
0.005796852077 1
< 0.1%
0.00871438499 1
< 0.1%
0.01110923771 1
< 0.1%
0.01912358986 1
< 0.1%
0.02366180362 1
< 0.1%
0.02713880685 1
< 0.1%
0.02773611111 1
< 0.1%
0.02871527778 1
< 0.1%
ValueCountFrequency (%)
215585.1969 1
 
< 0.1%
118086.5914 1
 
< 0.1%
118034.1458 1
 
< 0.1%
118032.4041 1
 
< 0.1%
118026.3421 1
 
< 0.1%
118017.1726 1
 
< 0.1%
118014.5285 1
 
< 0.1%
118010.4898 1
 
< 0.1%
118006.4988 6
< 0.1%
118001.9287 6
< 0.1%

DO
Real number (ℝ)

Distinct19409
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.483089
Minimum0
Maximum354.97759
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:17.970582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.6670184
Q18.2260217
median9.6660955
Q311.095766
95-th percentile13.169993
Maximum354.97759
Range354.97759
Interquartile range (IQR)2.869744

Descriptive statistics

Standard deviation16.81019
Coefficient of variation (CV)1.6035532
Kurtosis296.20879
Mean10.483089
Median Absolute Deviation (MAD)1.4369149
Skewness16.943946
Sum245786.49
Variance282.58249
MonotonicityNot monotonic
2023-02-02T16:39:18.031412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.708430556 11
 
< 0.1%
12.08062847 10
 
< 0.1%
9.987256944 10
 
< 0.1%
8.924236111 9
 
< 0.1%
8.059923611 9
 
< 0.1%
9.389659722 9
 
< 0.1%
7.731243056 9
 
< 0.1%
4.162380764 9
 
< 0.1%
3.218755378 9
 
< 0.1%
8.680909722 9
 
< 0.1%
Other values (19399) 23352
99.6%
ValueCountFrequency (%)
0 3
< 0.1%
0.2234930556 1
 
< 0.1%
0.2289583333 1
 
< 0.1%
0.2346944444 1
 
< 0.1%
0.535875 1
 
< 0.1%
0.5426909722 1
 
< 0.1%
0.5442743056 1
 
< 0.1%
0.5455208333 1
 
< 0.1%
0.5476666667 1
 
< 0.1%
0.5570833333 1
 
< 0.1%
ValueCountFrequency (%)
354.9775903 1
 
< 0.1%
350.844588 1
 
< 0.1%
350.2903487 1
 
< 0.1%
348.9448664 5
< 0.1%
345.6860347 1
 
< 0.1%
344.8827361 1
 
< 0.1%
344.8493507 4
< 0.1%
343.2838435 1
 
< 0.1%
342.7693021 1
 
< 0.1%
339.5286148 1
 
< 0.1%

pH
Real number (ℝ)

Distinct19078
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5137891
Minimum0.0025
Maximum13.790451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:18.091945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0025
5-th percentile5.7870911
Q17.1478749
median7.6311563
Q37.9749115
95-th percentile8.3077426
Maximum13.790451
Range13.787951
Interquartile range (IQR)0.8270366

Descriptive statistics

Standard deviation0.87651541
Coefficient of variation (CV)0.11665425
Kurtosis12.338036
Mean7.5137891
Median Absolute Deviation (MAD)0.40040672
Skewness-0.19135221
Sum176168.3
Variance0.76827926
MonotonicityNot monotonic
2023-02-02T16:39:18.149691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.960267361 11
 
< 0.1%
7.9386875 10
 
< 0.1%
7.701305556 10
 
< 0.1%
8.078440972 10
 
< 0.1%
9.183684028 10
 
< 0.1%
6.944145833 10
 
< 0.1%
7.885038194 9
 
< 0.1%
7.507246528 9
 
< 0.1%
7.674159722 9
 
< 0.1%
7.505371528 9
 
< 0.1%
Other values (19068) 23349
99.6%
ValueCountFrequency (%)
0.0025 1
 
< 0.1%
0.02 1
 
< 0.1%
0.04 3
< 0.1%
0.04670833333 1
 
< 0.1%
0.06 2
< 0.1%
0.0805625 1
 
< 0.1%
0.09507638889 1
 
< 0.1%
0.09948611111 1
 
< 0.1%
0.1049166667 1
 
< 0.1%
0.1092361111 1
 
< 0.1%
ValueCountFrequency (%)
13.79045139 1
< 0.1%
13.68230208 1
< 0.1%
13.60482168 1
< 0.1%
13.50673628 1
< 0.1%
13.49504167 1
< 0.1%
13.47515278 1
< 0.1%
13.45948958 1
< 0.1%
13.41638647 1
< 0.1%
13.37937153 1
< 0.1%
13.36761084 1
< 0.1%

chlorophyll
Real number (ℝ)

SKEWED  ZEROS 

Distinct18271
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.532824
Minimum0
Maximum59462.899
Zeros1529
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:18.217323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.46566319
median1.2444427
Q34.2930165
95-th percentile102.89022
Maximum59462.899
Range59462.899
Interquartile range (IQR)3.8273533

Descriptive statistics

Standard deviation930.88464
Coefficient of variation (CV)23.547133
Kurtosis2866.5414
Mean39.532824
Median Absolute Deviation (MAD)1.043849
Skewness51.655831
Sum926886.58
Variance866546.21
MonotonicityNot monotonic
2023-02-02T16:39:18.277349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1529
 
6.5%
2.829520833 10
 
< 0.1%
1.342516154 10
 
< 0.1%
0.7168263889 9
 
< 0.1%
5.652826389 9
 
< 0.1%
8.596649306 9
 
< 0.1%
0.7444097222 9
 
< 0.1%
29.24377083 8
 
< 0.1%
2.651560881 8
 
< 0.1%
1.154545139 8
 
< 0.1%
Other values (18261) 21837
93.1%
ValueCountFrequency (%)
0 1529
6.5%
0.0009201388889 1
 
< 0.1%
0.0009513888889 1
 
< 0.1%
0.001169544893 1
 
< 0.1%
0.001333984445 1
 
< 0.1%
0.001489583333 1
 
< 0.1%
0.001690751445 1
 
< 0.1%
0.001847222222 1
 
< 0.1%
0.002135416667 1
 
< 0.1%
0.002392361111 1
 
< 0.1%
ValueCountFrequency (%)
59462.8986 1
< 0.1%
58945.07041 1
< 0.1%
54365.8061 1
< 0.1%
53169.06921 1
< 0.1%
47121.93829 1
< 0.1%
35084.85759 1
< 0.1%
31630.10413 1
< 0.1%
27480.42869 1
< 0.1%
26948.5032 1
< 0.1%
25381.5971 1
< 0.1%

turbidity
Real number (ℝ)

Distinct14734
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8006177
Minimum0
Maximum99.988639
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:18.343474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31123611
Q11.0980454
median2.683687
Q39.5134653
95-th percentile47.898694
Maximum99.988639
Range99.988639
Interquartile range (IQR)8.4154199

Descriptive statistics

Standard deviation16.964078
Coefficient of variation (CV)1.7309192
Kurtosis8.9260767
Mean9.8006177
Median Absolute Deviation (MAD)2.1232321
Skewness2.9105889
Sum229785.28
Variance287.77994
MonotonicityNot monotonic
2023-02-02T16:39:18.403597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.307083333 20
 
0.1%
11.51238194 19
 
0.1%
6.210263889 19
 
0.1%
78.25515278 19
 
0.1%
6.770125 18
 
0.1%
2.968525281 18
 
0.1%
51.03280556 18
 
0.1%
18.27320833 18
 
0.1%
9.185739583 18
 
0.1%
1.348585823 18
 
0.1%
Other values (14724) 23261
99.2%
ValueCountFrequency (%)
0 9
< 0.1%
0.0001319444444 1
 
< 0.1%
0.0005158678381 1
 
< 0.1%
0.001059459694 1
 
< 0.1%
0.001180555556 1
 
< 0.1%
0.001286269279 1
 
< 0.1%
0.002308008809 1
 
< 0.1%
0.003083333333 1
 
< 0.1%
0.003191978547 1
 
< 0.1%
0.003732638889 1
 
< 0.1%
ValueCountFrequency (%)
99.98863889 1
 
< 0.1%
99.89586806 1
 
< 0.1%
99.87997851 1
 
< 0.1%
99.85890278 1
 
< 0.1%
99.46018056 1
 
< 0.1%
99.44445139 6
< 0.1%
99.37908333 1
 
< 0.1%
99.35345486 1
 
< 0.1%
99.07136458 1
 
< 0.1%
98.91891262 1
 
< 0.1%

fDOM
Real number (ℝ)

Distinct16179
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.379228
Minimum0.0016666667
Maximum298.34492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:18.466775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0016666667
5-th percentile1.184276
Q15.9634218
median15.809764
Q335.193319
95-th percentile88.596172
Maximum298.34492
Range298.34326
Interquartile range (IQR)29.229898

Descriptive statistics

Standard deviation34.655744
Coefficient of variation (CV)1.2657678
Kurtosis12.715532
Mean27.379228
Median Absolute Deviation (MAD)11.616319
Skewness3.0377977
Sum641933.38
Variance1201.0206
MonotonicityNot monotonic
2023-02-02T16:39:18.523801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.45 22
 
0.1%
45.22 19
 
0.1%
27.7235625 14
 
0.1%
1.601944444 14
 
0.1%
15.12207447 14
 
0.1%
25.38077778 14
 
0.1%
20.45387955 14
 
0.1%
109.2923125 13
 
0.1%
13.39816667 13
 
0.1%
0.4514305556 13
 
0.1%
Other values (16169) 23296
99.4%
ValueCountFrequency (%)
0.001666666667 1
 
< 0.1%
0.001807628524 1
 
< 0.1%
0.00725 1
 
< 0.1%
0.009034722222 1
 
< 0.1%
0.009215277778 1
 
< 0.1%
0.01093055556 1
 
< 0.1%
0.01320138889 11
< 0.1%
0.01429861111 1
 
< 0.1%
0.01854861111 1
 
< 0.1%
0.01926388889 1
 
< 0.1%
ValueCountFrequency (%)
298.3449236 1
 
< 0.1%
295.7704514 1
 
< 0.1%
295.0840134 1
 
< 0.1%
292.856303 1
 
< 0.1%
291.973625 9
< 0.1%
287.3111135 1
 
< 0.1%
284.1592708 1
 
< 0.1%
283.9141528 1
 
< 0.1%
283.8414861 1
 
< 0.1%
283.0132083 1
 
< 0.1%

mean_temp
Real number (ℝ)

Distinct17328
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.89726
Minimum1.9013284
Maximum31.721088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:18.589885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.9013284
5-th percentile3.2524922
Q17.109186
median12.124423
Q318.377882
95-th percentile23.883193
Maximum31.721088
Range29.819759
Interquartile range (IQR)11.268696

Descriptive statistics

Standard deviation6.7680836
Coefficient of variation (CV)0.52476911
Kurtosis-1.0284933
Mean12.89726
Median Absolute Deviation (MAD)5.5913361
Skewness0.26025395
Sum302389.16
Variance45.806956
MonotonicityNot monotonic
2023-02-02T16:39:18.643868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.302480655 49
 
0.2%
4.348202381 12
 
0.1%
16.21949648 12
 
0.1%
3.839376488 12
 
0.1%
10.03405655 12
 
0.1%
21.83341964 12
 
0.1%
7.926578656 12
 
0.1%
9.31 12
 
0.1%
4.677092458 12
 
0.1%
11.32598958 12
 
0.1%
Other values (17318) 23289
99.3%
ValueCountFrequency (%)
1.901328383 1
 
< 0.1%
1.903630952 1
 
< 0.1%
1.911272321 1
 
< 0.1%
1.911690476 1
 
< 0.1%
1.911869048 1
 
< 0.1%
1.912936012 1
 
< 0.1%
1.913133929 1
 
< 0.1%
1.916729167 1
 
< 0.1%
1.917998512 6
< 0.1%
1.920572917 1
 
< 0.1%
ValueCountFrequency (%)
31.7210878 1
< 0.1%
31.24484673 1
< 0.1%
31.05685565 1
< 0.1%
31.03692708 1
< 0.1%
30.89431548 1
< 0.1%
30.84168155 1
< 0.1%
30.71561905 1
< 0.1%
30.71248214 1
< 0.1%
30.58959821 1
< 0.1%
30.57940774 1
< 0.1%

CH4_conc
Real number (ℝ)

Distinct733
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.588224
Minimum1.3109407
Maximum1121.0379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:18.795243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.3109407
5-th percentile1.7898706
Q12.2888922
median2.9568271
Q36.5429576
95-th percentile46.94904
Maximum1121.0379
Range1119.727
Interquartile range (IQR)4.2540654

Descriptive statistics

Standard deviation79.515231
Coefficient of variation (CV)4.7934748
Kurtosis143.0286
Mean16.588224
Median Absolute Deviation (MAD)0.9100072
Skewness11.210174
Sum388927.49
Variance6322.672
MonotonicityNot monotonic
2023-02-02T16:39:18.852700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.60143328 80
 
0.3%
3.71680165 80
 
0.3%
2.658842758 80
 
0.3%
2.512820683 70
 
0.3%
2.363340225 67
 
0.3%
1.7453277 55
 
0.2%
2.34971865 55
 
0.2%
1121.0379 54
 
0.2%
19.9759255 51
 
0.2%
1.74231325 51
 
0.2%
Other values (723) 22803
97.3%
ValueCountFrequency (%)
1.310940717 32
0.1%
1.34711825 33
0.1%
1.347839808 35
0.1%
1.36316455 31
0.1%
1.457755 30
0.1%
1.478376767 35
0.1%
1.517455375 39
0.2%
1.534133375 37
0.2%
1.56372325 43
0.2%
1.569164775 31
0.1%
ValueCountFrequency (%)
1121.0379 54
0.2%
1099.988239 38
0.2%
507.196405 31
0.1%
406.1683071 31
0.1%
359.0325085 31
0.1%
348.6181527 31
0.1%
327.7892796 31
0.1%
292.5923172 30
0.1%
278.9625211 30
0.1%
272.9822939 31
0.1%

CO2_conc
Real number (ℝ)

Distinct733
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1303.4592
Minimum451.65826
Maximum11310.682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:18.916424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum451.65826
5-th percentile607.96726
Q1740.02833
median925.39724
Q31308.5466
95-th percentile3981.2353
Maximum11310.682
Range10859.024
Interquartile range (IQR)568.51831

Descriptive statistics

Standard deviation1173.8768
Coefficient of variation (CV)0.90058574
Kurtosis17.864518
Mean1303.4592
Median Absolute Deviation (MAD)230.10609
Skewness3.7830308
Sum30560905
Variance1377986.7
MonotonicityNot monotonic
2023-02-02T16:39:18.979851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1999.974753 80
 
0.3%
652.4161503 80
 
0.3%
921.5851002 80
 
0.3%
628.4179888 70
 
0.3%
675.6038126 67
 
0.3%
1030.656109 55
 
0.2%
726.9184898 55
 
0.2%
4111.138738 54
 
0.2%
1376.575813 51
 
0.2%
846.5851382 51
 
0.2%
Other values (723) 22803
97.3%
ValueCountFrequency (%)
451.658259 31
0.1%
468.3741605 30
0.1%
488.551533 33
0.1%
511.0961165 30
0.1%
517.5393612 36
0.2%
523.3695863 35
0.1%
530.5988639 31
0.1%
537.6745123 31
0.1%
538.5238653 30
0.1%
539.3033726 34
0.1%
ValueCountFrequency (%)
11310.68249 34
0.1%
8602.955041 29
0.1%
8470.422789 30
0.1%
7771.652797 32
0.1%
7545.073112 32
0.1%
7353.3547 40
0.2%
7223.292009 43
0.2%
6719.990008 30
0.1%
6232.460603 31
0.1%
6007.864268 31
0.1%

N2O_conc
Real number (ℝ)

Distinct733
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64399083
Minimum0.22668198
Maximum17.283047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:19.040608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.22668198
5-th percentile0.36388555
Q10.4552329
median0.55103137
Q30.68916245
95-th percentile1.0130376
Maximum17.283047
Range17.056366
Interquartile range (IQR)0.23392955

Descriptive statistics

Standard deviation0.77868384
Coefficient of variation (CV)1.2091536
Kurtosis320.71324
Mean0.64399083
Median Absolute Deviation (MAD)0.11167857
Skewness16.694549
Sum15099.009
Variance0.60634852
MonotonicityNot monotonic
2023-02-02T16:39:19.098139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9247087083 80
 
0.3%
0.721567075 80
 
0.3%
0.6338136583 80
 
0.3%
0.78556025 70
 
0.3%
0.62038335 67
 
0.3%
0.8191546 55
 
0.2%
0.5856849625 55
 
0.2%
0.761729675 54
 
0.2%
0.49425745 51
 
0.2%
0.4826633 51
 
0.2%
Other values (723) 22803
97.3%
ValueCountFrequency (%)
0.226681975 40
0.2%
0.2495983375 34
0.1%
0.2542558375 31
0.1%
0.2703872 8
 
< 0.1%
0.2727544 32
0.1%
0.2744453444 39
0.2%
0.2811430167 24
0.1%
0.2830501167 31
0.1%
0.28345535 30
0.1%
0.2839153167 23
0.1%
ValueCountFrequency (%)
17.2830475 31
0.1%
11.27185817 30
0.1%
6.1993433 31
0.1%
2.70986205 33
0.1%
2.41977485 30
0.1%
1.997072 33
0.1%
1.675499875 30
0.1%
1.54271025 31
0.1%
1.42676645 31
0.1%
1.384639 29
0.1%

Microbialabundanceper_ml
Real number (ℝ)

Distinct635
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2469686
Minimum4548.8889
Maximum62606310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-02T16:39:19.164661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4548.8889
5-th percentile46090
Q1162276.67
median328395.56
Q31115775.6
95-th percentile14852501
Maximum62606310
Range62601761
Interquartile range (IQR)953498.89

Descriptive statistics

Standard deviation6659992.6
Coefficient of variation (CV)2.6966961
Kurtosis32.50246
Mean2469686
Median Absolute Deviation (MAD)234294.44
Skewness5.0595365
Sum5.7904257 × 1010
Variance4.4355502 × 1013
MonotonicityNot monotonic
2023-02-02T16:39:19.225187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
295747.7778 193
 
0.8%
1281893.333 148
 
0.6%
165637.7778 139
 
0.6%
75857.77778 119
 
0.5%
332715.5556 110
 
0.5%
1223800 110
 
0.5%
593895.5556 102
 
0.4%
458984.4444 99
 
0.4%
320713.3333 95
 
0.4%
2596957.778 94
 
0.4%
Other values (625) 22237
94.8%
ValueCountFrequency (%)
4548.888889 37
0.2%
9002.222222 39
0.2%
9048.888889 35
0.1%
12447.77778 33
0.1%
12975.55556 44
0.2%
13443.33333 31
0.1%
14207.77778 43
0.2%
16585.55556 35
0.1%
17764.44444 31
0.1%
19081.11111 37
0.2%
ValueCountFrequency (%)
62606310 33
0.1%
61982167.78 31
0.1%
59389574.44 40
0.2%
39993141.11 33
0.1%
39849108.89 30
0.1%
39465021.11 32
0.1%
35361682.22 33
0.1%
32692272.22 33
0.1%
32397805.56 33
0.1%
30390946.67 31
0.1%

Interactions

2023-02-02T16:39:16.602303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:07.835076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.641466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.388451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.273442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.056933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.808677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.728034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.461623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.279842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.005888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.818436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.659227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:07.892519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.702323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.448406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.333607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.115178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.994800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.785546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.519354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.337218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.063031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.879944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.722650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:07.952633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.762501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.512073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.393418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.181494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.062518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.843002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.583010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.399168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.122709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.944458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.785874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.015506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.825645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.579173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.459601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.244462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.135254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.907020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.646867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.459303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.184112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.008338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.848890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.164428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.887705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.642554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.520048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.309584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.205524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.968776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.709767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.521187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.244093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.071824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.912291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.224440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.947836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.709037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.592576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.372164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.278338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.031028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.770995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.582824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.308254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.137369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.975353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.281607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.008579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.769866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.658582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.432601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.344855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.089892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.916311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.640393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.371397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.203674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:17.039203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.341949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.069023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.836371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.727373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.493492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.411042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.150367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.976894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.700941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.432159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.283405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:17.100015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.402019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.129101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.899961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.793916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.557239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.474949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.208741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.037612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.757935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.490333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.346989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:17.163012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.459787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.189615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.068277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.864923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.620257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.535349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.269749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.095059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.818743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.550872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.407852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:17.227088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.519838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.249763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.131231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.928504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.680842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.597951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.336705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.152266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.879369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.608757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.468443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:17.290468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:08.580836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:09.321432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.203858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:10.992314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:11.744819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:12.662491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:13.397299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.216280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:14.942375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:15.758917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-02T16:39:16.535239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2023-02-02T16:39:17.380654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-02T16:39:17.482778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

siteidnitrate_meanspecific_conductanceDOpHchlorophyllturbidityfDOMmean_tempCH4_concCO2_concN2O_concMicrobialabundanceper_ml
date
2018-01-01ARIK13.700000539.05944410.1863897.8225002.0950002.89944445.2200002.30248125.7928471621.7227010.49118652674.444444
2018-01-02ARIK13.700000539.05944410.1863897.8225002.0950002.89944445.2200002.30248125.7928471621.7227010.49118652674.444444
2018-01-03ARIK13.700000539.05944410.1863897.8225002.0950002.89944445.2200002.30248125.7928471621.7227010.49118652674.444444
2018-01-04ARIK12.535417546.4800429.9217857.8124033.24946560.36984045.2200002.30248125.7928471621.7227010.49118652674.444444
2018-01-05ARIK10.310417550.8302089.7217297.8267435.5652362.54800745.2200002.30248125.7928471621.7227010.49118652674.444444
2018-01-06ARIK9.966667535.34356910.0605287.8392228.42205633.21916745.2200002.30248125.7928471621.7227010.49118652674.444444
2018-01-07ARIK9.207292503.49446510.3337017.8834799.2041673.41703545.2200002.30248125.7928471621.7227010.49118652674.444444
2018-01-08ARIK8.103125486.38858210.6450007.90935412.09032619.39932645.2200002.30248125.7928471621.7227010.49118652674.444444
2018-01-09ARIK7.708333475.40259210.6507157.92991043.9297364.27802817.5900002.30248125.7928471621.7227010.49118652674.444444
2018-01-10ARIK6.932292467.58849310.5777437.94794430.26864611.8849100.4514312.30248125.7928471621.7227010.49118652674.444444
siteidnitrate_meanspecific_conductanceDOpHchlorophyllturbidityfDOMmean_tempCH4_concCO2_concN2O_concMicrobialabundanceper_ml
date
2020-12-22WLOU3.808333110.89398310.4145078.0930031.3441671.8892784.58103515.1607532.807956805.8041450.72599554252.222222
2020-12-23WLOU3.822917111.12055610.4083098.1041460.9894414.4692334.63829919.0594262.807956805.8041450.72599554252.222222
2020-12-24WLOU3.650000111.48186910.4959768.1035000.9282382.3897504.5185007.9265792.807956805.8041450.72599554252.222222
2020-12-25WLOU54.90208368.81119910.6192087.3442992.6619200.34067427.6552644.4804902.807956805.8041450.72599554252.222222
2020-12-26WLOU27.848958630.31836811.7336117.8204930.2548090.7221018.41608121.2526032.807956805.8041450.72599554252.222222
2020-12-27WLOU0.000000298.29189211.8997408.1460280.2200420.95909716.21523617.3897862.807956805.8041450.72599554252.222222
2020-12-28WLOU0.04175812.1051468.3842536.6516260.9006842.63997631.65502810.1731462.807956805.8041450.72599554252.222222
2020-12-29WLOU4.188542613.85351210.9006087.8691089.8175000.6004204.0829174.3258742.807956805.8041450.72599554252.222222
2020-12-30WLOU54.432292176.73539914.5619517.9488061.6422812.160620142.7745833.8393762.807956805.8041450.72599554252.222222
2020-12-31WLOU54.08020811.79975712.4422927.61187210.6312530.66855254.2513338.3642472.807956805.8041450.72599554252.222222

Duplicate rows

Most frequently occurring

siteidnitrate_meanspecific_conductanceDOpHchlorophyllturbidityfDOMmean_tempCH4_concCO2_concN2O_concMicrobialabundanceper_ml# duplicates
0ARIK13.7539.05944410.1863897.82252.0952.89944445.222.30248125.7928471621.7227010.49118652674.4444443